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Title: Boosting Iris Recognition by Margin-Based Loss Functions
Authors: Alinia Lat, R
Danishvar, S
Heravi, H
Danishvar, M
Keywords: biometrics;machine learning;convolutional neural networks;deep learning;iris recognition;margin-based loss functions
Issue Date: 29-Mar-2022
Publisher: MDPI AG
Citation: Alinia Lat, R., Danishvar, S., Heravi, H. and Danishvar, M. (2022) ‘Boosting Iris Recognition by Margin-Based Loss Functions’, Algorithms, 15 (4), 118, pp. 1-13. doi: 10.3390/a15040118.
Abstract: Copyright: © 2022 by the authors. In recent years, the topic of contactless biometric identification has gained considerable traction due to the COVID-19 pandemic. One of the most well-known identification technologies is iris recognition. Determining the classification threshold for large datasets of iris images remains challenging. To solve this issue, it is essential to extract more discriminatory features from iris images. Choosing the appropriate loss function to enhance discrimination power is one of the most significant factors in deep learning networks. This paper proposes a novel iris identification framework that integrates the light-weight MobileNet architecture with customized ArcFace and Triplet loss functions. By combining two loss functions, it is possible to improve the compactness within a class and the discrepancies between classes. To reduce the amount of preprocessing, the normalization step is omitted and segmented iris images are used directly. In contrast to the original SoftMax loss, the EER for the combined loss from ArcFace and Triplet is decreased from 1.11% to 0.45%, and the TPR is increased from 99.77% to 100%. In CASIA-Iris-Thousand, EER decreased from 4.8% to 1.87%, while TPR improved from 97.42% to 99.66%. Experiments have demonstrated that the proposed approach with customized loss using ArcFace and Triplet can significantly improve state-of-the-art and achieve outstanding results.
Description: Data Availability Statement: The analysed datasets are publicly available. Related references are reported in the References section. Acknowledgments: The authors would like to thank Guowei Wang for providing the implementation of Keras_insightface, which is available on Github, accessed on April 2021 ( leondgarse/Keras_insightface/ access on 25 April 2021).
Other Identifiers: 118
Appears in Collections:Dept of Mechanical and Aerospace Engineering Research Papers

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